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Certified Audit Data Scientist - increase audit quality

Certificate course


Technological changes such as Artificial Intelligence (AI) and the Internet of Things (IoT), are taking place at a rapid pace and lead to the fact that extensive and at the same time complex data sets can be digitally recorded and processed in the shortest possible time. Today, algorithms such as artificial neural networks perform tasks that until recently were reserved for experienced experts. This paradigm shift has led to the insights gained from data increasingly serving as the basis for answering far-reaching business management questions. Modern methods of data analysis in combination with powerful digital information systems offer new possibilities for evaluating structured and unstructured data.

Unknown Data patterns

The expectations of stakeholders are continuously evolving regarding the use of technology in audit practice and forensic analysis of business data sets. The new emerging discipline of "audit data science" describes the ability to analyse complex relationships in business process and accounting data. In addition, it encompasses the detection of inconsistencies and useful information in the data underlying an audit subject. As a result, "Audit Data Science" bundles a broad spectrum of knowledge from different disciplines, such as accounting, auditing, statistics, computer science and machine learning.

Programme Structure

The certificate course "Certified Audit Data Scientist" offers a comprehensive and practice-oriented introduction to this exciting subject area. It shows the correlation of the different disciplines to use them profitably in combination. In particular, the course teaches technical skills for practical application in the areas of fraud, auditing, compliance, or forensics. Examples of the latest technological advances and their use are discussed, such as "visual analytics" (continuous audit) and "deep learning" (anomaly detection). The course is held in German.

Auditing and forensic data analyses (module 1)

Theoretical part:

  • Introduction, standards and guidelines of analytical testing procedures
  • Techniques of auditing and forensic data analysis
  • Procedure, analysis planning, data acquisition, tools and reporting

Practical part:

  • Presentation and installation of the analysis environment
  • Introduction to Python programming and Jupyter notebooks
  • Application examples of rule-based analysis methods

Data analysis with Python and documentation of the analysis steps (module 2)

Theoretical part:

  • Procedure of data preparation, validation and import
  • Revisional data analysis with the programming language Python I
  • Revisional documentation of the analysis procedure and the analysis steps

Practical part:

  • Data preparation, validation and import procedure
  • Relationships and linkages of data properties
  • One- and multi-dimensional frequency and distribution analyses

Risk-oriented and rule-based analysis method (module 3)

Theoretical part:

  • Relation and linkage of data properties     
  • Auditing data analysis with the programming language Python II
  • Business processes in Enterprise Resource Planning (ERP) systems

Practical part:

  • Data models, tables and table fields in ERP systems
  • Risk-oriented analyses in financial accounting, purchasing and sales
  • Reporting on analysis procedures and results

Statistics and analytic methods based on artificial intelligence (module 4)

Theoretical part:

  • Introduction Artificial Intelligence, Machine Learning and Deep Learning
  • Elements, structure and functioning of artificial neural networks
  • Current and future challenges, e.g. "High Risk" systems and "DeepFakes”

Practical part:

  • Implementation, training and validation of deep neural networks
  • Detection of booking and process anomalies through autoencoder networks
  • Interpretation and communication of analysis results

Exploratory and visual analytic methods (module 5)

Theoretical part:

  • Introduction to current visualisation software
  • Explorative analyses in the context of high-dimensional data
  • Interactive presentation and "storytelling" using dashboards

Practical part:

  • Low code data pre-processing
  • Creation of explorative visual analyses
  • Implementation of meaningful dashboards

Key Facts


Certified Audit Data Scientist (CADS) (Frankfurt School of Finance & Management)

Target Group

Employees from compliance, internal audit, risk management and internal control system departments, as well as consultants and auditors in state authorities.


The certificate course comprises five seminar days with eight web sessions. A total of five exams, one practical paper and one practical exam are on the schedule.

Each module day begins with a theory section. The newly acquired knowledge is then applied in the practical part (second half of the day).


Total fee, including the exam and the certification: 6,900 Euros
All amounts are exempt from VAT.


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